Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations45000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 MiB
Average record size in memory80.0 B

Variable types

Numeric8
Boolean1
Categorical1

Alerts

cb_person_cred_hist_length is highly overall correlated with person_age and 1 other fieldsHigh correlation
loan_amnt is highly overall correlated with loan_percent_incomeHigh correlation
loan_percent_income is highly overall correlated with loan_amntHigh correlation
loan_status is highly overall correlated with previous_loan_defaults_on_fileHigh correlation
person_age is highly overall correlated with cb_person_cred_hist_length and 1 other fieldsHigh correlation
person_emp_exp is highly overall correlated with cb_person_cred_hist_length and 1 other fieldsHigh correlation
previous_loan_defaults_on_file is highly overall correlated with loan_statusHigh correlation
person_income is highly skewed (γ1 = 34.13758313) Skewed
person_emp_exp has 9566 (21.3%) zeros Zeros

Reproduction

Analysis started2025-02-17 16:15:30.675191
Analysis finished2025-02-17 16:16:08.145306
Duration37.47 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

person_age
Real number (ℝ)

High correlation 

Distinct60
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.764178
Minimum20
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2025-02-17T17:16:08.767660image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile22
Q124
median26
Q330
95-th percentile39
Maximum144
Range124
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.0451082
Coefficient of variation (CV)0.2177305
Kurtosis18.649449
Mean27.764178
Median Absolute Deviation (MAD)3
Skewness2.548154
Sum1249388
Variance36.543333
MonotonicityNot monotonic
2025-02-17T17:16:10.009951image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 5254
11.7%
24 5138
11.4%
25 4507
10.0%
22 4236
9.4%
26 3659
 
8.1%
27 3095
 
6.9%
28 2728
 
6.1%
29 2455
 
5.5%
30 2021
 
4.5%
31 1645
 
3.7%
Other values (50) 10262
22.8%
ValueCountFrequency (%)
20 17
 
< 0.1%
21 1289
 
2.9%
22 4236
9.4%
23 5254
11.7%
24 5138
11.4%
25 4507
10.0%
26 3659
8.1%
27 3095
6.9%
28 2728
6.1%
29 2455
5.5%
ValueCountFrequency (%)
144 3
< 0.1%
123 2
< 0.1%
116 1
 
< 0.1%
109 1
 
< 0.1%
94 1
 
< 0.1%
84 1
 
< 0.1%
80 1
 
< 0.1%
78 1
 
< 0.1%
76 1
 
< 0.1%
73 3
< 0.1%

person_income
Real number (ℝ)

Skewed 

Distinct33989
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80319.053
Minimum8000
Maximum7200766
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2025-02-17T17:16:10.910806image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum8000
5-th percentile28366.7
Q147204
median67048
Q395789.25
95-th percentile166754.7
Maximum7200766
Range7192766
Interquartile range (IQR)48585.25

Descriptive statistics

Standard deviation80422.499
Coefficient of variation (CV)1.0012879
Kurtosis2398.6848
Mean80319.053
Median Absolute Deviation (MAD)23124
Skewness34.137583
Sum3.6143574 × 109
Variance6.4677783 × 109
MonotonicityNot monotonic
2025-02-17T17:16:11.862775image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8000 15
 
< 0.1%
73011 10
 
< 0.1%
36995 9
 
< 0.1%
37020 8
 
< 0.1%
60914 8
 
< 0.1%
36946 7
 
< 0.1%
73040 7
 
< 0.1%
53638 7
 
< 0.1%
60864 7
 
< 0.1%
73082 7
 
< 0.1%
Other values (33979) 44915
99.8%
ValueCountFrequency (%)
8000 15
< 0.1%
8037 1
 
< 0.1%
8104 1
 
< 0.1%
8186 1
 
< 0.1%
8248 1
 
< 0.1%
8267 1
 
< 0.1%
8277 1
 
< 0.1%
8302 1
 
< 0.1%
8518 1
 
< 0.1%
9364 1
 
< 0.1%
ValueCountFrequency (%)
7200766 1
< 0.1%
5556399 1
< 0.1%
5545545 1
< 0.1%
2448661 1
< 0.1%
2280980 1
< 0.1%
2139143 1
< 0.1%
2012954 1
< 0.1%
1741243 1
< 0.1%
1728974 1
< 0.1%
1661567 1
< 0.1%

person_emp_exp
Real number (ℝ)

High correlation  Zeros 

Distinct63
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4103333
Minimum0
Maximum125
Zeros9566
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2025-02-17T17:16:13.160315image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q38
95-th percentile17
Maximum125
Range125
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.0635321
Coefficient of variation (CV)1.1207317
Kurtosis19.168324
Mean5.4103333
Median Absolute Deviation (MAD)3
Skewness2.5949174
Sum243465
Variance36.766421
MonotonicityNot monotonic
2025-02-17T17:16:14.905436image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9566
21.3%
2 4134
9.2%
1 4061
9.0%
3 3890
8.6%
4 3524
 
7.8%
5 3000
 
6.7%
6 2717
 
6.0%
7 2204
 
4.9%
8 1890
 
4.2%
9 1575
 
3.5%
Other values (53) 8439
18.8%
ValueCountFrequency (%)
0 9566
21.3%
1 4061
9.0%
2 4134
9.2%
3 3890
8.6%
4 3524
 
7.8%
5 3000
 
6.7%
6 2717
 
6.0%
7 2204
 
4.9%
8 1890
 
4.2%
9 1575
 
3.5%
ValueCountFrequency (%)
125 1
< 0.1%
124 1
< 0.1%
121 1
< 0.1%
101 1
< 0.1%
100 1
< 0.1%
93 1
< 0.1%
85 1
< 0.1%
76 1
< 0.1%
62 1
< 0.1%
61 1
< 0.1%

loan_amnt
Real number (ℝ)

High correlation 

Distinct4483
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9583.1576
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2025-02-17T17:16:15.972908image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2000
Q15000
median8000
Q312237.25
95-th percentile24000
Maximum35000
Range34500
Interquartile range (IQR)7237.25

Descriptive statistics

Standard deviation6314.8867
Coefficient of variation (CV)0.65895678
Kurtosis1.3512152
Mean9583.1576
Median Absolute Deviation (MAD)3800
Skewness1.1797313
Sum4.3124209 × 108
Variance39877794
MonotonicityNot monotonic
2025-02-17T17:16:16.958813image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 3617
 
8.0%
5000 2787
 
6.2%
6000 2426
 
5.4%
12000 2416
 
5.4%
15000 2004
 
4.5%
8000 1928
 
4.3%
4000 1406
 
3.1%
20000 1385
 
3.1%
3000 1378
 
3.1%
7000 1314
 
2.9%
Other values (4473) 24339
54.1%
ValueCountFrequency (%)
500 5
< 0.1%
563 1
 
< 0.1%
700 1
 
< 0.1%
725 1
 
< 0.1%
750 1
 
< 0.1%
800 1
 
< 0.1%
900 2
 
< 0.1%
912 1
 
< 0.1%
922 1
 
< 0.1%
950 1
 
< 0.1%
ValueCountFrequency (%)
35000 234
0.5%
34826 1
 
< 0.1%
34800 1
 
< 0.1%
34664 1
 
< 0.1%
34375 1
 
< 0.1%
34322 1
 
< 0.1%
34121 1
 
< 0.1%
34000 4
 
< 0.1%
33950 2
 
< 0.1%
33800 1
 
< 0.1%

loan_int_rate
Real number (ℝ)

Distinct1302
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.006606
Minimum5.42
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2025-02-17T17:16:17.751230image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum5.42
5-th percentile6.17
Q18.59
median11.01
Q312.99
95-th percentile16
Maximum20
Range14.58
Interquartile range (IQR)4.4

Descriptive statistics

Standard deviation2.9788083
Coefficient of variation (CV)0.27063823
Kurtosis-0.42033531
Mean11.006606
Median Absolute Deviation (MAD)2.13
Skewness0.21378407
Sum495297.26
Variance8.8732988
MonotonicityNot monotonic
2025-02-17T17:16:18.481162image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.01 3329
 
7.4%
10.99 804
 
1.8%
7.51 798
 
1.8%
7.49 687
 
1.5%
7.88 673
 
1.5%
5.42 608
 
1.4%
7.9 606
 
1.3%
11.49 514
 
1.1%
9.99 484
 
1.1%
13.49 475
 
1.1%
Other values (1292) 36022
80.0%
ValueCountFrequency (%)
5.42 608
1.4%
5.43 2
 
< 0.1%
5.44 2
 
< 0.1%
5.46 1
 
< 0.1%
5.47 5
 
< 0.1%
5.48 4
 
< 0.1%
5.49 4
 
< 0.1%
5.5 1
 
< 0.1%
5.51 3
 
< 0.1%
5.52 2
 
< 0.1%
ValueCountFrequency (%)
20 84
0.2%
19.91 9
 
< 0.1%
19.9 1
 
< 0.1%
19.82 5
 
< 0.1%
19.8 1
 
< 0.1%
19.79 4
 
< 0.1%
19.74 4
 
< 0.1%
19.69 12
 
< 0.1%
19.66 3
 
< 0.1%
19.62 1
 
< 0.1%

loan_percent_income
Real number (ℝ)

High correlation 

Distinct64
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13972489
Minimum0
Maximum0.66
Zeros27
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2025-02-17T17:16:19.596790image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03
Q10.07
median0.12
Q30.19
95-th percentile0.31
Maximum0.66
Range0.66
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.087212308
Coefficient of variation (CV)0.6241716
Kurtosis1.0824162
Mean0.13972489
Median Absolute Deviation (MAD)0.05
Skewness1.0345122
Sum6287.62
Variance0.0076059867
MonotonicityNot monotonic
2025-02-17T17:16:20.493336image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08 2593
 
5.8%
0.1 2421
 
5.4%
0.07 2415
 
5.4%
0.09 2295
 
5.1%
0.06 2242
 
5.0%
0.12 2216
 
4.9%
0.05 2176
 
4.8%
0.11 2158
 
4.8%
0.14 1960
 
4.4%
0.04 1950
 
4.3%
Other values (54) 22574
50.2%
ValueCountFrequency (%)
0 27
 
0.1%
0.01 315
 
0.7%
0.02 944
 
2.1%
0.03 1488
3.3%
0.04 1950
4.3%
0.05 2176
4.8%
0.06 2242
5.0%
0.07 2415
5.4%
0.08 2593
5.8%
0.09 2295
5.1%
ValueCountFrequency (%)
0.66 1
 
< 0.1%
0.63 1
 
< 0.1%
0.62 2
 
< 0.1%
0.61 2
 
< 0.1%
0.59 1
 
< 0.1%
0.58 1
 
< 0.1%
0.57 1
 
< 0.1%
0.56 5
< 0.1%
0.55 5
< 0.1%
0.54 8
< 0.1%

cb_person_cred_hist_length
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8674889
Minimum2
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2025-02-17T17:16:21.050736image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median4
Q38
95-th percentile14
Maximum30
Range28
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.8797018
Coefficient of variation (CV)0.66122014
Kurtosis3.7259445
Mean5.8674889
Median Absolute Deviation (MAD)2
Skewness1.63172
Sum264037
Variance15.052086
MonotonicityNot monotonic
2025-02-17T17:16:21.843686image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4 8653
19.2%
3 8312
18.5%
2 6537
14.5%
5 3082
 
6.8%
6 2966
 
6.6%
7 2889
 
6.4%
8 2800
 
6.2%
9 2685
 
6.0%
10 2457
 
5.5%
12 715
 
1.6%
Other values (19) 3904
8.7%
ValueCountFrequency (%)
2 6537
14.5%
3 8312
18.5%
4 8653
19.2%
5 3082
 
6.8%
6 2966
 
6.6%
7 2889
 
6.4%
8 2800
 
6.2%
9 2685
 
6.0%
10 2457
 
5.5%
11 712
 
1.6%
ValueCountFrequency (%)
30 23
0.1%
29 15
< 0.1%
28 29
0.1%
27 23
0.1%
26 20
< 0.1%
25 23
0.1%
24 34
0.1%
23 26
0.1%
22 32
0.1%
21 24
0.1%

credit_score
Real number (ℝ)

Distinct340
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean632.60876
Minimum390
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2025-02-17T17:16:22.642588image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum390
5-th percentile539
Q1601
median640
Q3670
95-th percentile703
Maximum850
Range460
Interquartile range (IQR)69

Descriptive statistics

Standard deviation50.435865
Coefficient of variation (CV)0.079726789
Kurtosis0.20302186
Mean632.60876
Median Absolute Deviation (MAD)33
Skewness-0.61026083
Sum28467394
Variance2543.7765
MonotonicityNot monotonic
2025-02-17T17:16:23.441024image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
658 406
 
0.9%
649 398
 
0.9%
652 396
 
0.9%
663 394
 
0.9%
647 393
 
0.9%
654 391
 
0.9%
650 391
 
0.9%
667 390
 
0.9%
653 390
 
0.9%
656 386
 
0.9%
Other values (330) 41065
91.3%
ValueCountFrequency (%)
390 1
 
< 0.1%
418 1
 
< 0.1%
419 1
 
< 0.1%
420 1
 
< 0.1%
421 1
 
< 0.1%
430 1
 
< 0.1%
431 2
< 0.1%
434 1
 
< 0.1%
435 4
< 0.1%
437 2
< 0.1%
ValueCountFrequency (%)
850 1
< 0.1%
807 1
< 0.1%
805 1
< 0.1%
792 1
< 0.1%
789 1
< 0.1%
784 2
< 0.1%
773 1
< 0.1%
772 1
< 0.1%
770 1
< 0.1%
768 1
< 0.1%

previous_loan_defaults_on_file
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
True
22858 
False
22142 
ValueCountFrequency (%)
True 22858
50.8%
False 22142
49.2%
2025-02-17T17:16:23.903227image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

loan_status
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.7 KiB
0
35000 
1
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 35000
77.8%
1 10000
 
22.2%

Length

2025-02-17T17:16:24.486904image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-17T17:16:25.217292image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 35000
77.8%
1 10000
 
22.2%

Most occurring characters

ValueCountFrequency (%)
0 35000
77.8%
1 10000
 
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 35000
77.8%
1 10000
 
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 35000
77.8%
1 10000
 
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 35000
77.8%
1 10000
 
22.2%

Interactions

2025-02-17T17:16:01.318489image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:33.103606image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:37.094013image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:40.651917image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:43.816872image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:47.700390image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:51.831771image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:57.312015image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:01.620608image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:33.550715image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:37.478284image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:40.978051image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:44.211080image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:48.225654image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:52.358770image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:57.986751image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:02.212562image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:34.040452image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:37.878411image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:41.343152image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:44.700799image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:48.764494image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:53.126501image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:58.467238image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:02.731125image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:34.411606image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:38.655141image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:41.690489image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:45.054066image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:49.414804image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:54.163963image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:58.936027image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:03.429799image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:34.788157image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:39.089080image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:42.297251image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:45.484141image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:49.877471image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:55.666486image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:59.492830image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:03.879671image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:35.350266image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:39.562692image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:42.655519image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:46.195255image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:50.313291image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:56.032512image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:59.993805image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:04.320000image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:36.327359image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:39.954558image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:43.017473image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:46.667290image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:50.943146image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:56.452879image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:00.435570image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:04.697240image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:36.780214image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:40.344617image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:43.470675image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:47.103455image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:51.351177image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:15:56.939092image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:00.955182image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Correlations

2025-02-17T17:16:25.512364image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
cb_person_cred_hist_lengthcredit_scoreloan_amntloan_int_rateloan_percent_incomeloan_statusperson_ageperson_emp_expperson_incomeprevious_loan_defaults_on_file
cb_person_cred_hist_length1.0000.1420.0430.017-0.0370.0200.8210.7500.0930.026
credit_score0.1421.0000.0060.011-0.0120.0080.1600.1720.0230.178
loan_amnt0.0430.0061.0000.1050.6660.1260.0640.0520.4050.066
loan_int_rate0.0170.0110.1051.0000.1240.3630.0130.016-0.0330.198
loan_percent_income-0.037-0.0120.6660.1241.0000.415-0.056-0.050-0.3530.220
loan_status0.0200.0080.1260.3630.4151.0000.0120.0140.0090.543
person_age0.8210.1600.0640.013-0.0560.0121.0000.8880.1430.030
person_emp_exp0.7500.1720.0520.016-0.0500.0140.8881.0000.1200.028
person_income0.0930.0230.405-0.033-0.3530.0090.1430.1201.0000.008
previous_loan_defaults_on_file0.0260.1780.0660.1980.2200.5430.0300.0280.0081.000

Missing values

2025-02-17T17:16:05.245040image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-17T17:16:07.199958image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

person_ageperson_incomeperson_emp_exploan_amntloan_int_rateloan_percent_incomecb_person_cred_hist_lengthcredit_scoreprevious_loan_defaults_on_fileloan_status
022.071948.0035000.016.020.493.0561No1
121.012282.001000.011.140.082.0504Yes0
225.012438.035500.012.870.443.0635No1
323.079753.0035000.015.230.442.0675No1
424.066135.0135000.014.270.534.0586No1
521.012951.002500.07.140.192.0532No1
626.093471.0135000.012.420.373.0701No1
724.095550.0535000.011.110.374.0585No1
824.0100684.0335000.08.900.352.0544No1
921.012739.001600.014.740.133.0640No1
person_ageperson_incomeperson_emp_exploan_amntloan_int_rateloan_percent_incomecb_person_cred_hist_lengthcredit_scoreprevious_loan_defaults_on_fileloan_status
4499031.0136832.0912319.016.920.097.0722No1
4499124.037786.0013500.013.430.364.0612No1
4499223.040925.009000.011.010.224.0487No1
4499327.035512.045000.015.830.145.0505No1
4499424.031924.0212229.010.700.384.0678No1
4499527.047971.0615000.015.660.313.0645No1
4499637.065800.0179000.014.070.1411.0621No1
4499733.056942.072771.010.020.0510.0668No1
4499829.033164.0412000.013.230.366.0604No1
4499924.051609.016665.017.050.133.0628No1